Nearest centroid classification on a trapped ion quantum computer

TitleNearest centroid classification on a trapped ion quantum computer
Publication TypeJournal Article
Year of Publication2021
AuthorsS Johri, S Debnath, A Mocherla, A Singk, A Prakash, J Kim, and I Kerenidis
JournalNpj Quantum Information
Volume7
Issue1
Date Published12/2021
Abstract

Quantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.

DOI10.1038/s41534-021-00456-5
Short TitleNpj Quantum Information